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相关概念视频

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Sample Proportion and Population Proportion01:20

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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Systematic Sampling Method01:17

Systematic Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Convenience Sampling Method00:55

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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相关实验视频

Updated: Sep 9, 2025

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
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有限人口调查抽样:一个不抱歉的贝叶斯观点

Sudipto Banerjee1

  • 1University of California Los Angeles, Los Angeles, USA.

Sankhya. Series A. (2008)
|September 2, 2025
PubMed
概括
此摘要是机器生成的。

这项研究探讨了具有复杂依赖性的有限群体的贝叶斯推理. 它介绍了处理单位关系和响应机制的方法,增强了统计建模能力.

关键词:
贝叶斯推理主要的 62F15二次性 62D05有限人口调查抽样图形模型层次模型空间数据

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科学领域:

  • 统计数据
  • 统计推理
  • 计算统计

背景情况:

  • 有限的人口抽样通常假定独立的单位,在许多复杂的场景中这是不现实的.
  • 贝叶斯层次模型提供了一个灵活的框架,用于整合先前的信息和复杂的数据结构.
  • 现有的方法可能无法充分解决人口单位之间的依赖关系.

研究的目的:

  • 为具有复杂依赖性的有限人口量提供贝叶斯推理的观点.
  • 扩展推理框架以适应依赖单位和不可忽视的响应.
  • 使用图形模型和空间过程来说明应用程序.

主要方法:

  • 贝叶斯层次模型的概述,包括产生霍维茨-普森估计器的模型.
  • 在依赖有限群体中引入可忽视和不可忽视的响应机制的框架.
  • 使用图形模型和空间过程应用多变量依赖关系.

主要成果:

  • 在有限种群中证明复杂依赖的推理框架.
  • 介绍处理可忽略和不可忽略响应的方法.
  • 对空间有限群体的说明性分析,展示讨论的方法.

结论:

  • 贝叶斯推理为具有复杂依赖性的有限群体提供了一个强大的方法.
  • 拟议的框架增强了对依赖数据结构的建模和分析能力.
  • 图形模型和空间过程是理解多变量依赖性的有价值的工具.